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Python module for extracting optical physiology ROIs and traces for various file types and formats

Project description

SegmentationExtractors

Python-based module for extracting from, converting between, and handling recorded and optical imaging data from several file formats. Inspired by SpikeExtractors. image

Getting Started:

Installation (<=python3.6) :

pip install segmentationextractors

Installation (>=python3.7) :

Will need a manual installation for package dependency SIMA since it does not currently support python 3.7:

  1. Download SIMA wheels distribution here.
  2. pip install <download-path-to-wheels.whl>
  3. pip install segmentationextractors

Usage:

Currently supported file types:

  1. calciumImagingAnalysis (CNMF-E, EXTRACT),
  2. SIMA,
  3. NWB,
  4. Numpy (a data format for manual input of optical physiology data as various numpy datasets)

Functionality:

Interconversion amongst the various data formats as well as conversion to the NWB format and back.

Features:

  1. SegmentationExtractor object:
    • seg_obj.get_channel_names() : List of optical channel names
    • seg_obj.get_num_channels() : Number of channels
    • seg_obj.get_movie_framesize(): (height, width) of raw movie
    • seg_obj.get_movie_location(): Location of storage of movie/tiff images
    • seg_obj.get_image_masks(self, ROI_ids=None): Image masks as (ht, wd, num_rois) with each value as the weight given during segmentation operation.
    • seg_obj.get_pixel_masks(ROI_ids=None): Get pixel masks as (total_pixels(ht*wid), no_rois)
    • seg_obj.get_traces(self, ROI_ids=None, start_frame=None, end_frame=None): df/F trace as (num_rois, num_frames)
    • seg_obj.get_sampling_frequency(): Sampling frequency of movie/df/F trace.
    • seg_obj.get_roi_locations(): Centroid pixel location of the ROI (Regions Of Interest) as (x,y).
    • seg_obj.get_num_rois(): Total number of ROIs after segmentation operation.
    • seg_obj.get_roi_ids(): Any integer tags associated with an ROI, defaults to 0:num_of_rois

SegmentationExtractor object creation:

import segmentationextractors
seg_obj_cnmfe = segmentationextractors.CnmfeSegmentationExtractor('cnmfe_filename.mat') # cnmfe
seg_obj_extract = segmentationextractors.ExtractSegmentationExtractor('extract_filename.mat') # extract
seg_obj_sima = segmentationextractors.SimaSegmentationExtractor('sima_filename.sima') # SIMA
seg_obj_numpy = segmentationextractors.NumpySegmentationExtractor(
                    filepath = 'path-to-file',
                    masks=np.random.rand(movie_size[0],movie_size[1],no_rois),
                    signal=np.random.randn(num_rois,num_frames),
                    roi_idx=np.random.randint(no_rois,size=[1,no_rois]),
                    no_of_channels=None,
                    summary_image=None,
                    channel_names=['Blue']) # Numpy object
seg_obj_nwb = segmentationextractors.NwbSegmentationExtractor(
                    filepath_of_nwb, optical_channel_name=None, # optical channel to extract and store info from
                    imaging_plane_name=None, image_series_name=None, # imaging plane to extract and store data from
                    processing_module_name=None,
                    neuron_roi_response_series_name=None, # roi_response_series name to extract and store data from
                    background_roi_response_series_name=None) # nwb object

Data format conversion: SegmentationExtractor to NWB:

    segmentationextractors.NwbSegmentationExtractor.write_recording(seg_obj, saveloc,
                        propertydict=[{'name': 'ROI feature 1,
                                       'description': 'additional attribute of each ROI',
                                       'data': np.random.rand(1,no_rois),
                                       'id': seg_obj.get_roi_ids()},
                                      {'name': 'ROI feature 2,
                                       'description': 'additional attribute of each ROI',
                                       'data': np.random.rand(1,no_rois),
                                       'id': seg_obj.get_roi_ids()}],
                        nwbfile_kwargs={'session_description': 'nwbfiledesc',
                                        'experimenter': 'experimenter name',
                                        'lab': 'test lab',
                                        'session_id': 'test sess id'},
                        emission_lambda=400.0, excitation_lambda=500.0)

Example Datasets:

  • Example datasets for each of the file formats can be downloaded here.

Class description:

  • SegmentationExtractor:

  • An abstract class that contains all the meta-data and output data from the ROI segmentation operation when applied to the pre-processed data. It also contains methods to read from and write to various data formats ouput from the processing pipelines like SIMA, CaImAn, Suite2p, CNNM-E.

  • NumpySegmentationExtractor:

  • NumpySegmentationExtractor objects are built to contain all data coming from a file format for which there is currently no support. To construct this, all data must be entered manually as arguments.

  • CnmfeSegmentationExtractor:

  • This class inherits from the SegmentationExtractor class, having all its funtionality specifically applied to the dataset output from the 'CNMF-E' ROI segmentation method.

  • ExtractSegmentationExtractor:

  • This class inherits from the SegmentationExtractor class, having all its funtionality specifically applied to the dataset output from the 'EXTRACT' ROI segmentation method.

  • SimaSegmentationExtractor:

  • This class inherits from the SegmentationExtractor class, having all its funtionality specifically applied to the dataset output from the 'SIMA' ROI segmentation method.

  • NwbSegmentationExtractor:

  • Class used to extract data from the NWB data format. Also implements a static method to write any format specific object to NWB.

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